{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to Optimize Performance\n",
    "\n",
    "This guide shows you how to make your BrainPy simulations run faster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick Wins\n",
    "\n",
    "**Top 5 optimizations (80% of speedup):**\n",
    "\n",
    "1. ✅ **Use JIT compilation** - 10-100× speedup\n",
    "2. ✅ **Use sparse connectivity** - 10-100× memory reduction\n",
    "3. ✅ **Batch operations** - 2-10× speedup on GPU\n",
    "4. ✅ **Use GPU/TPU** - 10-100× speedup for large networks\n",
    "5. ✅ **Minimize Python loops** - Use JAX operations instead"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## JIT Compilation\n",
    "\n",
    "**Essential for performance!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import brainstate\n",
    "\n",
    "# Slow (no JIT)\n",
    "def slow_step(net, inp):\n",
    "    return net(inp)\n",
    "\n",
    "# Fast (with JIT)\n",
    "@brainstate.transform.jit\n",
    "def fast_step(net, inp):\n",
    "    return net(inp)\n",
    "\n",
    "# Warmup (compilation)\n",
    "_ = fast_step(net, inp)\n",
    "\n",
    "# 10-100× faster than slow_step\n",
    "output = fast_step(net, inp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Rules for JIT:**\n",
    "- Static shapes (no dynamic array sizes)\n",
    "- Pure functions (no side effects)\n",
    "- Avoid Python loops over data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sparse Connectivity\n",
    "\n",
    "**Biological networks are sparse (~1-10% connectivity)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dense: 10,000 × 10,000 = 100M connections (400MB)\n",
    "comm_dense = brainstate.nn.Linear(10000, 10000)\n",
    "\n",
    "# Sparse: 10,000 × 10,000 × 0.01 = 1M connections (4MB)\n",
    "comm_sparse = brainstate.nn.EventFixedProb(\n",
    "    10000, 10000,\n",
    "    prob=0.01,  # 1% connectivity\n",
    "    weight=0.5*u.mS\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Memory savings:** 100× for 1% connectivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batching\n",
    "\n",
    "**Process multiple trials in parallel:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sequential: 10 trials one by one\n",
    "for trial in range(10):\n",
    "    brainstate.nn.init_all_states(net)\n",
    "    run_trial(net)\n",
    "\n",
    "# Parallel: 10 trials simultaneously\n",
    "brainstate.nn.init_all_states(net, batch_size=10)\n",
    "run_batched(net)  # 5-10× faster on GPU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optimal batch sizes:**\n",
    "- CPU: 1-16\n",
    "- GPU: 32-256\n",
    "- TPU: 128-512"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GPU Usage\n",
    "\n",
    "**Automatic when available:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax\n",
    "print(jax.devices())  # Check for GPU\n",
    "\n",
    "# BrainPy automatically uses GPU\n",
    "net = brainpy.state.LIF(10000, ...)\n",
    "# Runs on GPU if available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**See:** GPU/TPU Usage guide for details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Avoid Python Loops\n",
    "\n",
    "**Replace Python loops with JAX operations:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SLOW: Python loop\n",
    "result = []\n",
    "for i in range(1000):\n",
    "    result.append(net(inp))\n",
    "\n",
    "# FAST: JAX loop\n",
    "def body_fun(i):\n",
    "    return net(inp)\n",
    "\n",
    "results = brainstate.transform.for_loop(body_fun, jnp.arange(1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Appropriate Precision\n",
    "\n",
    "**Float32 is usually sufficient:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Default (float32) - fast\n",
    "weights = jnp.ones((1000, 1000))  # 4 bytes/element\n",
    "\n",
    "# Float64 - 2× slower, 2× memory\n",
    "weights = jnp.ones((1000, 1000), dtype=jnp.float64)  # 8 bytes/element"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Minimize State Storage\n",
    "\n",
    "**Don't accumulate history:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# BAD: Stores all history in Python list\n",
    "history = []\n",
    "for t in range(10000):\n",
    "    output = net(inp)\n",
    "    history.append(output)  # Memory leak!\n",
    "\n",
    "# GOOD: Process on the fly\n",
    "for t in range(10000):\n",
    "    output = net(inp)\n",
    "    metrics = compute_metrics(output)  # Don't store raw data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimize Network Architecture\n",
    "\n",
    "**1. Use simpler neuron models when possible:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Complex (slow but realistic)\n",
    "neuron = brainpy.state.HH(1000, ...)  # Hodgkin-Huxley\n",
    "\n",
    "# Simple (fast)\n",
    "neuron = brainpy.state.LIF(1000, ...)  # Leaky Integrate-and-Fire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Use CUBA instead of COBA when possible:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slower (conductance-based)\n",
    "out = brainpy.state.COBA.desc(E=0*u.mV)\n",
    "\n",
    "# Faster (current-based)\n",
    "out = brainpy.state.CUBA.desc()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Reduce connectivity:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dense\n",
    "prob = 0.1  # 10% connectivity\n",
    "\n",
    "# Sparse\n",
    "prob = 0.02  # 2% connectivity (5× fewer connections)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Profile Before Optimizing\n",
    "\n",
    "**Identify actual bottlenecks:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "# Time different components\n",
    "start = time.time()\n",
    "for _ in range(100):\n",
    "    net(inp)\n",
    "print(f\"Network update: {time.time() - start:.2f}s\")\n",
    "\n",
    "start = time.time()\n",
    "for _ in range(100):\n",
    "    output = process_output(net.get_spike())\n",
    "print(f\"Output processing: {time.time() - start:.2f}s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Don't optimize blindly - measure first!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Checklist\n",
    "\n",
    "**For maximum performance:**\n",
    "\n",
    "```python\n",
    "✅ JIT compiled (@brainstate.transform.jit)\n",
    "✅ Sparse connectivity (EventFixedProb with prob < 0.1)\n",
    "✅ Batched (batch_size ≥ 32 on GPU)\n",
    "✅ GPU enabled (check jax.devices())\n",
    "✅ Static shapes (no dynamic array sizes)\n",
    "✅ Minimal history storage\n",
    "✅ Appropriate neuron models (LIF vs HH)\n",
    "✅ Float32 precision\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Common Bottlenecks\n",
    "\n",
    "**Issue 1: First run very slow**  \n",
    "   → JIT compilation happens on first call (warmup)\n",
    "\n",
    "**Issue 2: CPU-GPU transfers**  \n",
    "   → Keep data on GPU between operations\n",
    "\n",
    "**Issue 3: Small batch sizes**  \n",
    "   → Increase batch_size for better GPU utilization\n",
    "\n",
    "**Issue 4: Python loops**  \n",
    "   → Replace with JAX operations (for_loop, vmap)\n",
    "\n",
    "**Issue 5: Dense connectivity**  \n",
    "   → Use sparse (EventFixedProb) for large networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Complete Optimization Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import brainpy as bp\n",
    "import brainstate\n",
    "import brainunit as u\n",
    "import jax\n",
    "\n",
    "# Optimized network\n",
    "class OptimizedNetwork(brainstate.nn.Module):\n",
    "    def __init__(self, n_neurons=10000):\n",
    "        super().__init__()\n",
    "\n",
    "        # Simple neuron model\n",
    "        self.neurons = brainpy.state.LIF(n_neurons, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n",
    "\n",
    "        # Sparse connectivity\n",
    "        self.recurrent = brainpy.state.AlignPostProj(\n",
    "            comm=brainstate.nn.EventFixedProb(\n",
    "                n_neurons, n_neurons,\n",
    "                prob=0.01,  # Sparse!\n",
    "                weight=0.5*u.mS\n",
    "            ),\n",
    "            syn=brainpy.state.Expon.desc(n_neurons, tau=5*u.ms),\n",
    "            out=brainpy.state.CUBA.desc(),  # Simple output\n",
    "            post=self.neurons\n",
    "        )\n",
    "\n",
    "    def update(self, inp):\n",
    "        spk = self.neurons.get_spike()\n",
    "        self.recurrent(spk)\n",
    "        self.neurons(inp)\n",
    "        return spk\n",
    "\n",
    "# Initialize\n",
    "net = OptimizedNetwork()\n",
    "brainstate.nn.init_all_states(net, batch_size=64)  # Batched\n",
    "\n",
    "# JIT compile\n",
    "@brainstate.transform.jit\n",
    "def simulate_step(net, inp):\n",
    "    return net(inp)\n",
    "\n",
    "# Warmup\n",
    "inp = brainstate.random.rand(64, 10000) * 2.0 * u.nA\n",
    "_ = simulate_step(net, inp)\n",
    "\n",
    "# Fast simulation\n",
    "import time\n",
    "start = time.time()\n",
    "for _ in range(1000):\n",
    "    output = simulate_step(net, inp)\n",
    "elapsed = time.time() - start\n",
    "\n",
    "print(f\"Optimized: {1000/elapsed:.1f} steps/s\")\n",
    "print(f\"Throughput: {64*1000/elapsed:.1f} trials/s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark Results\n",
    "\n",
    "**Typical speedups from optimization:**\n",
    "\n",
    "| Optimization | Speedup | Cumulative |\n",
    "|--------------|---------|------------|\n",
    "| Baseline (Python loops, dense) | 1× | 1× |\n",
    "| + JIT compilation | 10-50× | 10-50× |\n",
    "| + Sparse connectivity | 2-10× | 20-500× |\n",
    "| + GPU | 5-20× | 100-10,000× |\n",
    "| + Batching | 2-5× | 200-50,000× |\n",
    "\n",
    "**Real example:** 10,000 neuron network\n",
    "- Baseline (CPU, no JIT): 0.5 steps/s\n",
    "- Optimized (GPU, JIT, sparse, batched): 5,000 steps/s\n",
    "- **Total speedup: 10,000×**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## See Also\n",
    "\n",
    "- Tutorials: Large-Scale Simulations\n",
    "- GPU/TPU Usage\n",
    "- Debugging Networks"
   ]
  }
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